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International audience; In this paper, we examine the changes in the dependence structure of global stock markets amid the outbreak of COVID-19. We divide 56 stock markets into developed, emerging, and frontier markets and study their daily price data from 15 October 2019 to 17 August 2020 using the canonical vine (C-vine) copula approach. We observed significant changes in the dependence structure, the selection of the pair copula families, and the associated parameter estimates in the tree. In developed markets, during the COVID-19, the dependence of markets shifted from the Netherlands to France while the root node position of Hungary replaced the market of Poland. The frontier markets showed strong dependence signs with Mauritius before COVID-19 and Slovenia during the outbreak. Our findings are of interest to regulators and practitioners, particularly in monitoring the value at risk of portfolios and adopting appropriate strategies in light of the varying dynamics of stock markets during extreme events, such as COVID-19.

Background: Senegal reported the first COVID-19 case on March 2, 2020. A nationwide cross-sectional epidemiological survey was conducted to capture the true extent of COVID-19 exposure. Methods: Multi-stage random cluster sampling of households was carried out between October 24 and November 26, 2020, at the end of the first wave of COVID-19 transmission. Anti-SARS-CoV-2 antibodies (IgG and/or IgM) were screened using three distinct ELISA assays. Adjusted prevalence for the survey design were calculated for each test separately, and thereafter combined. Crude, adjusted prevalence based on tests performances and weighted prevalence by sex-age strata were estimated to assess the seroprevalence. Findings: Of the 1,463 participants included in this study, 58·8% were women and the mean age of participants was 29·2 years (range 0·25–82·0). The national seroprevalence was estimated at 28 . 4% (95% CI: 26·1-30·8). There was substantial regional variability. Four regions recorded the highest seroprevalence: Ziguinchor (56·7%), Sedhiou (48·0%), Dakar (44·0%) and Kaolack (32·7%) whereas, Louga (11·1%) and Matam (11·2%), located in the Center-North, were less impacted in our analysis. All age groups were impacted and the prevalence of SARS-CoV-2 was comparable in symptomatic and asymptomatic groups. We estimated 4,744,392 SARS-CoV-2 (95% CI: 4,360,164 – 5,145,327) potential infected in Senegal compared to 16,089 COVID-19 RT-PCR laboratory-confirmed cases reported at the time of the survey. Interpretation: These results provide an estimate of SARS-CoV-2 virus dissemination in the Senegalese population. Preventive and control measures need to be reinforced in the country and especially in the south border regions. Funding Information: This work was supported by US Centers for Disease Control and Prevention (CDC), the Senegalese Ministry of Health, the Senegalese National Statistics and Demography Agency (ANSD), the WHO Unity program and the COVID-19 Task-force of the International Pasteur Institute Network (IPIN, REPAIR project). Declaration of Interests: We declare no competing interests. Ethics Approval Statement: All participants have consented to participate in the study. For people younger than 18 years, a legal representative provided informed consent. The study was approved by the Senegalese National Ethics Committee for Research in Health (reference number N°0176/MSAS/DPRS/CNERS, 10 October 2020).

This paper shows that prospect theory, extended to account for differences across individuals in their patience and their valuation of the vaccination as a common good can explain why more than 40% of the population has intent to reject the Covid-19 vaccination, as well as the differences in vaccination intentions across population subgroups. Indeed, prospect theory by over-weighting the side effect explains the reject of vaccination. This can be partially compensated by a high patience and/or a large valuation of the collective immunity. The calibrated version of our model, based on an original survey carried out on a representative sample of the adult population living in France allowing us to identify curvatures of their value function, their discount rates and their willingness to cooperate, can predict the evolution of the vaccination intentions between November 2020 an March 2021. We also show that the international differences in the vaccination intentions are closely related to the valuation of the vaccination as a common good.

Abstract Fast, sensitive, high-throughput detection of coronavirus antigens at physiologically relevant levels is essential for population screening that could prevent epidemics such as the current COVID-19 global pandemic. Optical methods based on surface-enhanced Raman scattering (SERS) spectroscopy are promising for this purpose because they ensure quick detection of even single biological molecules in a sample. For achieving such a high sensitivity, it is crucial to design SERS-active systems concentrating incident radiation into small sample volumes. Here, metal-dielectric cavities have been obtained through interaction of protein sulfhydryl groups with a SERS-active silver surface. The concentration of light in these cavities allows the differential detection of spike glycoprotein and nucleocapsid protein of SARS-COV-2, which are its key antigens, at physiologically relevant concentrations. The cavity Q-factor can be increased by additionally covering the dielectric protein film with a silver shell to form an ultrathin cavity, which provides an at least tenfold enhancement of the detection signal. The results could be used to design high-throughput systems for specific and sensitive detection of viral antigens and quick diagnosis of viral infections.

COVID-19 mortality increases markedly with age and is also substantially higher among Black, Indigenous, and People of Color (BIPOC) populations in the United States. These two facts can have conflicting implications because BIPOC populations are younger than white populations. In analyses of California and Minnesota—demographically divergent states—we show that COVID vaccination schedules based solely on age benefit the older white populations at the expense of younger BIPOC populations with higher risk of death from COVID-19. We find that strategies that prioritize high-risk geographic areas for vaccination at all ages better target mortality risk than age-based strategies alone, although they do not always perform as well as direct prioritization of high-risk racial/ethnic groups. Vaccination schemas directly implicate equitability of access, both domestically and globally. Age-based COVID-19 vaccination prioritizes white people above higher-risk others; geographic prioritization improves equity. Description

Abstract The global lockdowns brought on by the COVID-19 pandemic forced an immediate change in the way people moved about;namely, travel was slowed from a turbulent river to a trickle In-person meetings, often involving long-distance flights, were either canceled, postponed, or shifted over to virtual modes People who were unfamiliar with online meetings quickly became acquainted with them

International audience; In honour of the International Year of Sound and for the 1 year memorial of the Notre-Dame cathedral fire, a team of researchers and sound engineers created a virtual reconstruction of a concert in the cathedral, using close-mic recordings made on 24-April-2013 of a performance of La Vierge by Jules Massenet. This reconstruction was carried out during the period of strict COVID-19 confinement. With 83 musicians, 6 singers and a 160person choir spatially distributed throughout the cathedral, the original performance offered a spatial composition highlighting the complex acoustics and interactions between source and listener positions. Individual tracks were convolved with spatial room impulse responses, created from a calibrated geometrical acoustic simulation. Several listening positions were binaurally rendered, along with an artistic mix created by a sound engineer, offering a unique 3d-audio experience approaching the reality of the moment in the past. Distributed on-line, the website presenting this virtual reconstruction has been visited from users around the world. A listener opinion survey was included on the website. This paper presents an overview of the source material, the production workflow, and the challenges of realising such a production during confinement. Finally, an overview of visitor statistics and survey results is presented, providing insight into the reception of the virtual recreation and the interest towards future productions.

Pandemic disease is not merely a biological reality but also a cognitive and socially constructed phenomenon which intensely mobilizes a multiplicity of political frames. Far-right political entrepreneurs are, despite their remoteness from actual decision-making processes, active stakeholders in the current crisis. Existential threats to societies breed a sense of urgency and heightened cultural warfare that is a hotbed for extremism. Our study seeks to map, compare and contrast the symbolic responses to the Coronavirus crisis articulated by various far-right actors in two established democracies in the transatlantic area: The United States and France. We aim to shed light on how entrenched far-right mythologies and tropes—which appear increasingly transatlantic—are channeled into a new synthesis as part of an “alternative” political epistemology. Infused with the mythos of resistance and insurgency, resolutely anti-systemic, this alternative epistemology can better be described, following Michael Barkun, as a form of “stigmatized knowledge”. Our study will employ a Critical Discourse Analysis framework to bring into focus, in the response of the Euro-American far-right to the COVID-19 crisis, the ideological semiotics of the current “infodemic”.

International audience; This paper presents the Multilingual COVID-19 Analysis Method (CMTA) for detecting and observing the spread of misinformation about this disease within texts. CMTA proposes a data science (DS) pipeline that applies machine learning models for processing, classifying (Dense-CNN) and analyzing (MBERT) multilingual (micro)-texts. DS pipeline data preparation tasks extract features from multilingual textual data and categorize it into specific information classes (i.e., 'false', 'partly false', 'misleading'). The CMTA pipeline has been experimented with multilingual micro-texts (tweets), showing misinformation spread across different languages. To assess the performance of CMTA and put it in perspective, we performed a comparative analysis of CMTA with eight monolingual models used for detecting misinformation. The comparison shows that CMTA has surpassed various monolingual models and suggests that it can be used as a general method for detecting misinformation in multilingual micro-texts. CMTA experimental results show misinformation trends about COVID-19 in different languages during the first pandemic months.